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Fine-scale environmental suitability mapping of Aedes aegypti

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    • Date
    4 February 2026
    Timeframe
    16:00 - 17:00 CET
    Duration
    60 minutes
    • Days
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    Effective control of Aedes aegypti is critical for reducing the global burden of vector-borne diseases such as dengue fever, which causes an estimated 400 million infections and 40,000 deaths annually. This session will explore innovative approaches to mapping the spatiotemporal distribution of Ae. aegypti, with a focus on urban environments where most infections occur. Traditional sample-based entomological surveillance often fails to capture the fine-scale spatial variability of Ae. aegypti, driven by heterogeneous urban landscapes and the mosquitoes’ limited flight range. The presentation will highlight methods that leverage geospatial big data, including satellite imagery and street-level images, to identify common breeding habitats and predict seasonal suitability for eggs and larvae at a 200 m resolution in Rio de Janeiro, Brazil. Participants will learn how microhabitat and macrohabitat indicators can explain up to 74% of the observed variation in ovitrap and larval counts, and how spatiotemporal interpolations provide high-resolution insights into mosquito distribution that conventional surveillance cannot achieve. This session is ideal for researchers, public health professionals, and data scientists interested in AI-assisted mosquito monitoring, high-resolution urban mapping, and data-driven vector control strategies.

     

    Session Objectives:

    By the end of this session, participants will be able to:

    • Describe the habitat of Aedes aegypti and major dengue control measures.
    • Explain state-of-the-art and AI-assisted techniques for mosquito monitoring.
    • Apply computer vision models to detect common mosquito breeding containers.
    • Analyze and interpret the predictive power of environmental suitability indicators using entomological datasets.

    Recommended Mastery Level/Prerequisites:

    • Basic knowledge of vector-borne diseases.
    • Introductory understanding of machine learning or computer vision.
    • Familiarity with entomological data is advantageous but not required.
    • Experience with Bayesian spatiotemporal modeling is beneficial.
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